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Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow

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Title: Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow
Authors: Silva, VLS
Salinas, P
Jackson, MD
Pain, CC
Item Type: Journal Article
Abstract: A machine learning approach to accelerate convergence of the nonlinear solver in multiphase flow problems is presented here. The approach dynamically controls an acceleration method based on numerical relaxation. It is demonstrated in a Picard iterative solver but is applicable to other types of nonlinear solvers. The aim of the machine learning acceleration is to reduce the computational cost of the nonlinear solver by adjusting to the complexity/physics of the system. Using dimensionless parameters to train and control the machine learning enables the use of a simple two-dimensional layered reservoir for training, while also exploring a wide range of the parameter space. Hence, the training process is simplified and it does not need to be rerun when the machine learning acceleration is applied to other reservoir models. We show that the method can significantly reduce the number of nonlinear iterations without compromising the simulation results, including models that are considerably more complex than the training case.
Issue Date: 1-Oct-2021
Date of Acceptance: 1-Jun-2021
URI: http://hdl.handle.net/10044/1/92279
DOI: 10.1016/j.cma.2021.113989
ISSN: 0045-7825
Publisher: Elsevier
Start Page: 1
End Page: 17
Journal / Book Title: Computer Methods in Applied Mechanics and Engineering
Volume: 384
Copyright Statement: © 2021 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords: Science & Technology
Technology
Physical Sciences
Engineering, Multidisciplinary
Mathematics, Interdisciplinary Applications
Mechanics
Engineering
Mathematics
Nonlinear solver
Machine learning
Numerical relaxation
Multiphase flows
Porous media
2-PHASE FLOW
CROSS-FLOW
TRANSPORT
SIMULATION
BUOYANCY
Science & Technology
Technology
Physical Sciences
Engineering, Multidisciplinary
Mathematics, Interdisciplinary Applications
Mechanics
Engineering
Mathematics
Nonlinear solver
Machine learning
Numerical relaxation
Multiphase flows
Porous media
2-PHASE FLOW
CROSS-FLOW
TRANSPORT
SIMULATION
BUOYANCY
Applied Mathematics
01 Mathematical Sciences
09 Engineering
Publication Status: Published
Embargo Date: 2022-06-17
Article Number: ARTN 113989
Online Publication Date: 2021-06-18
Appears in Collections:Earth Science and Engineering



This item is licensed under a Creative Commons License Creative Commons